City Research Online

Phenotypic Integrated Framework for Classification of ADHD using fMRI

Riaz, A., Alonso, E. and Slabaugh, G.G. (2016). Phenotypic Integrated Framework for Classification of ADHD using fMRI. Paper presented at the International Conference on Image Analysis and Recognition (ICIAR 2016), 13-15 Jul 2016, Póvoa de Varzim, Portugal.

Abstract

Attention Deficit Hyperactive Disorder (ADHD) is one of the most common disorders affecting young children, and its underlying mechanism is not completely understood. This paper proposes a phenotypic integrated machine learning framework to investigate functional connectivity alterations between ADHD and control subjects not diagnosed with ADHD, employing fMRI data. Our aim is to apply computational techniques to (1) automatically classify a person’s fMRI signal as ADHD or control, (2) identify differences in functional connectivity of these two groups and (3) evaluate the importance of phenotypic information for classification. In the first stage of our framework, we determine the functional connectivity of brain regions by grouping brain activity using clustering algorithms. Next, we employ Elastic Net regression to select the most discriminant features from the dense functional brain network and integrate phenotypic information. Finally, a support vector machine classifier is trained to classify ADHD subjects vs. control. The proposed framework was evaluated on a public dataset ADHD-200, and our classification results outperform the state-of-the-art on some subsets of the data.

Publication Type: Conference or Workshop Item (Paper)
Additional Information: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords: ADHD, Density Clustering, Affinity Propagation, Elastic Net
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
R Medicine > RJ Pediatrics
Departments: School of Mathematics, Computer Science & Engineering > Computer Science
URI: http://openaccess.city.ac.uk/id/eprint/14265
[img]
Preview
Text - Accepted Version
Download (262kB) | Preview

Export

Downloads

Downloads per month over past year

View more statistics

Actions (login required)

Admin Login Admin Login